System and method for correlating electronic device identifiers and vehicle information

ABSTRACT

A system for monitoring vehicle traffic may include a camera positioned to capture images within a license plate detection zone, the images may represent license plates of vehicles. The system may include an electronic device identification sensor that detects and stores electronic device identifiers of electronic devices located within an electronic device detection zone, and a computing system that detects, using the images, a license plate ID of a vehicle, compare the license plate ID of the vehicle to a database of trusted vehicle license plate IDs, identifies the vehicle as a suspicious vehicle, the identification based at least in part on the comparison of the license plate ID of the vehicle to the database of trusted vehicle license plate IDs, and correlates the license plate ID of the vehicle with at least one of the plurality of stored electronic device identifiers.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. application patent Ser. No.17/688,340 filed Mar. 7, 2022 titled “System and Method for CorrelatingElectronic Device Identifiers and Vehicle Information” which is acontinuation of U.S. patent application Ser. No. 16/910,949 filed Jun.24, 2020 (U.S. Pat. No. 11,270,129) titled “System and Method forCorrelating Electronic Device Identifiers and Vehicle Information” whichclaims priority to and the benefit of U.S. Provisional ApplicationPatent Ser. No. 62/866,278 filed Jun. 25, 2019, the entire disclosuresof which are hereby incorporated by reference.

TECHNICAL FIELD

This disclosure relates generally to information correlation. Morespecifically, this disclosure relates to a system and method forcorrelating electronic device identifiers and vehicle information.

BACKGROUND

Many public and private areas, including airports, business parks,companies, border checkpoints, neighborhoods, etc. employ measures toenhance the safety of the people and property on the area premises. Forexample, some neighborhoods are gated and visitors to the communitiesmay be forced to check-in with a guard at a security gate prior to beingallowed into the neighborhood. Some neighborhoods employ a crime watchgroup that includes a group of concerned citizens who work together withlaw enforcement to help keep their neighborhood safe. Such a program mayrely on volunteers to patrol the neighborhood to help law enforcementdiscover and/or thwart suspicious and/or criminal activity. However,these and other conventional measures lack the ability to correlatecertain information that provides for enhanced identification, tracking,and notification of and/or to suspicious vehicles/individuals.

SUMMARY

In general, the present disclosure provides a system and method forcorrelating wireless network information.

In one aspect, a system for monitoring vehicle traffic may include atleast one camera positioned to capture a set of images within a licenseplate detection zone, at least some of the captured images representinglicense plates of a set of vehicles appearing within the camera's fieldof view. The system may also include at least one electronic deviceidentification sensor configured to detect and store a set of electronicdevice identifiers of electronic devices located within one or moreelectronic device detection zones. The system may also include one ormore non-transitory computer-readable storage media having storedthereon computer-executable instructions that, when executed by one ormore processors, cause a computing system to: detect, using the set ofimages, a license plate ID of a vehicle; compare the license plate ID ofthe vehicle to a database of trusted vehicle license plate IDs; identifythe vehicle as a suspicious vehicle, the identification based at leastin part on the comparison of the license plate ID of the vehicle to thedatabase of trusted vehicle license plate IDs; and correlate the licenseplate ID of the vehicle with at least one of the set of storedelectronic device identifiers.

In another aspect, a method may include capturing, using at least onecamera, a set of images within a license plate detection zone, at leastsome of the set of images representing license plates of a set ofvehicles appearing within the camera's field of view. The method mayalso include detecting and storing, using an electronic deviceidentification sensor, a set of electronic device identifiers ofelectronic devices located within one or more electronic devicedetection zones. The method may also include detecting, using the set ofimages, a license plate ID of a vehicle. The method may also includecomparing the license plate ID of the vehicle to a database of trustedvehicle license plate IDs, and identifying the vehicle as a suspiciousvehicle, the identification based at least in part on the comparison ofthe license plate ID of the vehicle to the database of trusted vehiclelicense plate IDs. The method may also include correlating the licenseplate ID of the vehicle with at least one of the set of storedelectronic device identifiers.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

Before undertaking the DETAILED DESCRIPTION below, it may beadvantageous to set forth definitions of certain words and phrases usedthroughout this patent document. The term “couple” and its derivativesrefer to any direct or indirect communication between two or moreelements, whether or not those elements are in physical contact with oneanother. The terms “transmit,” “receive,” and “communicate,” as well asderivatives thereof, encompass both direct and indirect communication.The terms “include” and “comprise,” as well as derivatives thereof, meaninclusion without limitation. The term “or” is inclusive, meaningand/or. The phrase “associated with,” as well as derivatives thereof,means to include, be included within, interconnect with, contain, becontained within, connect to or with, couple to or with, be communicablewith, cooperate with, interleave, juxtapose, be proximate to, be boundto or with, have, have a property of, have a relationship to or with, orthe like. The term “controller” means any device, system or part thereofthat controls at least one operation. Such a controller may beimplemented in hardware or a combination of hardware and software and/orfirmware. The functionality associated with any particular controllermay be centralized or distributed, whether locally or remotely. Thephrase “at least one of,” when used with a list of items, means thatdifferent combinations of one or more of the listed items may be used,and only one item in the list may be needed. For example, “at least oneof: A, B, and C” includes any of the following combinations: A, B, C, Aand B, A and C, B and C, and A and B and C.

Moreover, various functions described below can be implemented orsupported by one or more computer programs, each of which is formed fromcomputer readable program code and embodied in a computer readablemedium. The terms “application” and “program” refer to one or morecomputer programs, software components, sets of instructions,procedures, functions, objects, classes, instances, related data, or aportion thereof adapted for implementation in a suitable computerreadable program code. The phrase “computer readable program code”includes any type of computer code, including source code, object code,and executable code. The phrase “computer readable medium” includes anytype of medium capable of being accessed by a computer, such as readonly memory (ROM), random access memory (RAM), a hard disk drive, acompact disc (CD), a digital video disc (DVD), solid state drives(SSDs), flash, or any other type of memory. A “non-transitory” computerreadable medium excludes wired, wireless, optical, or othercommunication links that transport transitory electrical or othersignals. A non-transitory computer readable medium includes media wheredata can be permanently stored and media where data can be stored andlater overwritten, such as a rewritable optical disc or an erasablememory device.

Definitions for other certain words and phrases are provided throughoutthis patent document. Those of ordinary skill in the art shouldunderstand that in many if not most instances, such definitions apply toprior as well as future uses of such defined words and phrases.

It should be noted that the term “cellular media access control (MAC)address” may refer to a MAC, international mobile subscriber identity(IMSI), mobile station international subscriber directory number(MSISDN), enhanced network selection (ENS), or any other form of uniqueidentifying number.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure and its advantages,reference is now made to the following description, taken in conjunctionwith the accompanying drawings, in which:

FIG. 1 illustrates a high-level component diagram of an illustrativesystem architecture, according to certain embodiments of thisdisclosure;

FIG. 2 illustrates details pertaining to various components of theillustrative system architecture of FIG. 1 , according to certainembodiments of this disclosure;

FIG. 3 illustrates example method for monitoring vehicle traffic,according to certain embodiments of this disclosure;

FIG. 4 illustrates another example method for monitoring vehicletraffic, according to certain embodiments of this disclosure;

FIG. 5 illustrates example use interfaces presented on computing devicesduring monitoring vehicle traffic, according to certain embodiments ofthis disclosure;

FIG. 6 illustrates an example computer system according to certainembodiments of this disclosure.

DETAILED DESCRIPTION

Improvement is desired in the field of public safety for certain areas(e.g., neighborhood, airport, business park, border checkpoint, city,etc.). As discussed above, there are various measures that may beconventionally used, such as gated communities, neighborhood crime watchgroups, and so forth. However, the conventional measures lack efficiencyand accuracy in identifying suspicious vehicles/individuals andreporting of the suspicious vehicles/individuals, among other things. Insome instances, the conventional measures may fail to report thesuspicious vehicle/individual, altogether. The causes of the inefficientand/or failed reporting may be at least in part attributable to people(e.g., neighbors in a neighborhood) not having access to verifiedvehicle and/or personal information of an individual. Further, theconventional measures lack the ability to quickly, accurately, andautomatically identify the vehicle as a suspicious vehicle, correlatevehicle information (e.g., license plate identifier (ID)), electronicdevice information (e.g., electronic device identifier (ID)), faceinformation, etc., and/or perform a preventative action based on theidentification.

Take the following example for illustrative purposes. A neighbor maywitness an unknown vehicle drive through the neighborhood several timeswithin a given time period during a day. The neighbor may not recognizethe license plate ID or driver and may think about reporting the unknownvehicle to law enforcement. Instead, the neighbor may decide to proceedto do another activity. Subsequently, the person may burglarize a housein the neighborhood. Even if the neighbor attempted to lookup thelicense plate ID, and was able to find out information about an owner ofthe vehicle, the neighbor may not be able to determine whether thedriver of the vehicle is the actual owner, the neighbor may not be ableto determine whether the owner or driver is on a crime watch list, andso forth. Further, the neighbor may not be privy to the electronicdevice identifier of the electronic device the suspicious individual iscarrying or that is installed in the vehicle, which may be used to trackthe whereabouts of the individual/vehicle in a monitored area. Even if aneighbor obtains an electronic device identifier, there currently is notechnique for determining personal information associated with theelectronic device identifier. To reiterate, conventional techniques forpublic safety lack the ability to identify a suspiciousvehicle/individual and/or to correlate vehicle information, facialinformation, and/or electronic device identifiers of electronic devicesof the driver to make an informed decision quickly, accurately, andautomatically.

Aspects of the present disclosure relate to embodiments that overcomethe shortcomings described above. The present disclosure relates to asystem and method for correlating electronic device identifiers withvehicle information. The system may include one or more license platedetection zones, one or more electronic device detection zones, and/orone or more facial detection zones. The zones may be partially or whollyoverlapping and there may be multiple zones established that span adesired area (e.g., a neighborhood, a city block, a public/privateparking lot, any street, etc.). The license plate detection zones, theelectronic device detection zones, and/or the facial detection zones mayinclude devices that are communicatively coupled to one or morecomputing systems via a network. The license plate detection zones mayinclude one or more cameras configured to capture images of at leastlicense plates on vehicles that enter the license plate detection zone.The electronic device detection zone may include one or more electronicdevice identification sensors, such as a WiFi signal detection device ora Bluetooth® signal detection device. The electronic deviceidentification sensors may be configured to detect and store WiFiMachine Access Control (MAC) addresses, Bluetooth MAC addresses, and/orcellular MAC addresses (e.g., International Mobile Subscriber Identity(IMSI), Mobile Station International Subscriber Directory Number(MSISDN), and Electronic Serial Numbers (ESN)) of electronic devicesthat enter the electronic device detection zone based on the signalsemitted by the electronic devices. The facial detection zones mayinclude one or more cameras configured to capture images or digitalframes that are used to recognize a face. Any suitable MAC address maybe detected, and to that end, a MAC address may be any combination ofthe IDs described herein (e.g., MAC, MSISIDN, IMSI, ESN, etc.).

The computing system may analyze the images captured by the cameras anddetect a license plate identifier (ID) of a vehicle. The license plateID may be compared with trusted license plate IDs that are stored in adatabase. When there is not a trusted license plate ID that matches thelicense plate ID, the computing system may identify the vehicle as asuspicious vehicle. Then, the computing system may correlate the licenseplate ID of the vehicle with at least one of the stored electronicdevice identifiers. In some embodiments, the license plate ID and the atleast one of the stored electronic device identifiers may be correlatedwith a face of the individual. In some embodiments, personalinformation, such as name, address, Bluetooth MAC address, WiFi MACaddress, criminal record, whether the suspicious individual is on acrime watch list, etc. may be retrieved using the license plate ID orthe at least one of the stored electronic device identifiers that iscorrelated with the license plate ID of the suspicious vehicle.

The system may include several computer applications that may beaccessed by registered users of the system. For example, a clientapplication may be accessed by a computing device of a user, such as aneighbor in a neighborhood implementing the system. The clientapplication may present a user interface including an alert when asuspicious vehicle and/or individual is detected. The user interface maypresent several preventative actions for the user. For example, the usermay contact the suspicious individual using the personal information(e.g., send a threatening text message), notify law enforcement, and soforth. Accordingly, a client application may be accessed by a computingdevice of a law enforcer. The client application may present a userinterface including the notification that a suspicious vehicle and/orindividual is detected in the particular zones.

Take the following example of a setup of the system for illustrationpurposes. In a neighborhood, that may only be accessed by two entrances,license plate detection zones and electronic device detection zones maybe placed to cover both lanes at both entrances. In some instances, afacial detection zone may be placed at the entrances with the otherzones. Each vehicle may be correlated with each electronic device thatenters the neighborhood. Further, the recognized face may be correlatedwith the electronic device and the vehicle information. The housesinside the neighborhood may setup electronic device detection zonesand/or a facial detection zone inside their property to detectelectronic device IDs and/or faces and compare them with electronicdevice IDs and/or faces in a database that stores every correlation thathas been made by the system to date (including the most recentcorrelations of electronic device IDs, faces, and/or vehicles enteringthe neighborhood). The home owner may be notified via the clientapplication on their computing device if an electronic device and/orface is detected on their property. Further, in some embodiments, theindividual associated with the electronic device and/or face may benotified on the electronic device that the homeowner is aware of theirpresence. If a known criminal with a warrant is detected at either thezones at the entrance or at the zones at the homeowner's property, theappropriate law enforcement agency may be notified of their whereabouts.

The disclosed techniques provide numerous benefits over conventionalsystems. For example, the system provides efficient, accurate, andautomatic identification of suspicious vehicles and/or individuals.Further, the system enables correlating vehicle license plate IDs withelectronic device identifiers to enable enhanced detection and/orpreventative actions, such as directly communicating with the electronicdevice of the suspicious individual and/or notifying law enforcementusing the client application in real-time or near real-time when thesuspicious vehicle enters one or more zones. For example, once theelectronic device identifier is detected, a correlation may be obtainedwith a license plate ID to obtain personal information about the ownerthat enables contacting the owner directly and/or determining whetherthe owner is a criminal. The client application provides pertinentinformation pertaining to both the suspicious vehicle and/or individualin a single user interface without the user having to perform anysearches of the license plate ID or electronic device identifier. Assuch, in some embodiments, the disclosed techniques reduce processing,memory, and/or network resources by reducing searches that the user mayperform to find the information. Also, the disclosed techniques providean enhanced user interface that presents the suspicious vehicle and/orindividual information in single location, which may improve a user'sexperience using the computing device.

FIGS. 1 through 6 , discussed below, and the various embodiments used todescribe the principles of this disclosure are by way of illustrationonly and should not be construed in any way to limit the scope of thedisclosure.

FIG. 1 illustrates a high-level component diagram of an illustrativesystem architecture 100 according to certain embodiments of thisdisclosure. In some embodiments, the system architecture 100 may includea computing device 102 communicatively coupled to a cloud-basedcomputing system 116, one or more cameras 120, one or more electronicdevice identification sensors 130, and/or one or more electronic device140 of a suspicious individual. The cloud-based computing system 116 mayinclude one or more servers 118. Each of the computing device 102, theservers 118, the cameras 120, the electronic device identificationsensors 130, and the electronic device 140 may include one or moreprocessing devices, memory devices, and network interface devices.

The network interface devices may enable communication via a wirelessprotocol for transmitting data over short distances, such as Bluetooth,ZigBee, etc. Additionally, the network interface devices may enablecommunicating data over long distances, and in one example, thecomputing device 102 may communicate with a network 112. Network 112 maybe a public network (e.g., connected to the Internet via wired(Ethernet) or wireless (WiFi)), a private network (e.g., a local areanetwork (LAN) or wide area network (WAN)), or a combination thereof.

The computing device 102 may be any suitable computing device, such as alaptop, tablet, smartphone, or computer. The computing device may beconfigured to execute a client application 104 that presents a userinterface. The client application 104 may be implemented in computerinstructions stored on one or more memory devices and executed by one ormore processing devices of the computing device 102. The clientapplication 104 may be a standalone application installed on thecomputing device 102 or may be an application that is executed byanother application (e.g., a website in a web browser).

The computing device 102 may include a display that is capable ofpresenting the user interface of the client application 104. The userinterface may present various screens to a user depending on what typeof user is logged into the client application 104. For example, a user,such as a neighbor or person interested in a particular license platedetection zone 122 and/or electronic device detection zone 132, may bepresented with a user interface for logging into the system where theuser enters credentials (username and password), a user interface thatdisplays alerts of suspicious vehicles and/or individuals in the zones122 and/or 132 where the user interface includes options forpreventative actions, a user interface that presents logged events overtime, and so forth. For example, the client application 104 may enablethe user to directly contact (e.g., send text message, send email, call)the electronic device 140 of a suspicious individual 142 using personalinformation obtained about the individual 142. Another user, such as alaw enforcer, may be presented with a user interface for logging intothe system where the user enters credentials (username and password), auser interface that displays notifications when the user selects tonotify law enforcement where the notifications may include informationrelated to the suspicious vehicle and/or individual 142.

In some embodiments, the cameras 120 may be located in the license platedetection zones 122. Although just one camera 120 and one license platedetection zone 122 are depicted, it should be noted that any suitablenumber of cameras 120 may be located in any suitable number of licenseplate detection zones 122. For example, multiple license plate detectionzones 122 may be used to cover a desired area. A license plate detectionzone 122 may refer to an area of coverage that is within the cameras'120 field of view. The cameras 120 may be any suitable camera and/orvideo camera capable of capturing a set of images 123 that at leastrepresent license plates of a vehicle 126 that enters the license platedetection zone 122. The set of images 123 may be transmitted by thecamera 120 to the cloud-based computing system 116 and/or the computingdevice 102 via the network 112.

In some embodiments, the electronic device identification sensors 130may be located in the electronic device detection zones 132. In someembodiments, the license plate detection zone 122 and the electronicdevice detection zone 132-1 may partially or wholly overlap. Thecombination of license plate detection zones 122 and the electronicdevice detection zones 132 may be setup at entrances/exits to certainareas, and/or any other suitable area in a monitored area, to correlateeach vehicle information with respective electronic device identifiers133 of electronic devices 140 being carried in respective vehicles 126.Each of the license plate detection zones 122 and electronic devicedetection zones 132 may have unique geographic identifiers so the datacan be tracked by location. It should be noted that any suitable numberof electronic device identification sensors 130 may be located in anysuitable number of electronic device detection zones 132. For example,multiple electronic device detection zones 132 may be used to cover adesired area. An electronic device detection zone 132 may refer to anarea of coverage that is within the electronic device identificationsensor 130 detection area.

In one example, an electronic device detection zone 132-2 and/or afacial detection zone 150 may be setup at a home of a homeowner, suchthat an electronic device 140 and/or a face of a suspicious individual142 may be detected and stored when the suspicious individual 142 entersthe zone 132-2. The electronic device ID 133 and/or an image of the facemay be transmitted to the cloud-based computing device 116 or thecomputing device 102 via the network 112. In some instances, thesuspicious individual 142 may be contacted on their electronic device140 with a message indicating the homeowner is aware of their presenceand to leave the premises. In some instances, if a known criminalindividual 142 with a warrant is detected at the combination of zones122 and 132-1 at an entrance or at the zone 132-2 and 150 at the home,then the proper law enforcement agency may be contacted with thewhereabouts of the individual 142.

In some embodiments, the cameras 120 may be located in the facialdetection zones 150. Although just one camera 120 and one facialdetection zone 150 are depicted, it should be noted that any suitablenumber of cameras 120 may be located in any suitable number of facialdetection zones 122. For example, multiple facial detection zones 150may be used to cover a desired area. A facial detection zone 150 mayrefer to an area of coverage that is within the cameras' 120 field ofview. The cameras 120 may be any suitable camera and/or video cameracapable of capturing a set of images 123 that at least represent facesof an individual 142 that enters the facial detection zone 150. The setof images 123 may be transmitted by the camera 120 to the cloud-basedcomputing system 116 and/or the computing device 102 via the network112. In some embodiments, the cloud-based computing system 116 and/orthe computing device 102 may perform facial recognition by comparing aface detected in the image to a database of faces to find a match and/orperform biometric artificial intelligence that may uniquely identify anindividual 142 by analyzing patterns based on the individual's facialtextures and shape.

The electronic device identification sensors 130 may be configured todetect a set of electronic device IDs 133 (e.g., WiFi MAC addresses,Bluetooth MAC addresses, and/or cellular MAC addresses) of electronicdevice 140 within the electronic device detection zone 132. As depicted,the electronic device 140 of a suspicious individual is within thevehicle 126 passing through the electronic device detection zone 132.That is, the electronic device identification sensors 130 may be anysuitable WiFi signal detection device capable of detecting WiFi MACaddresses and/or Bluetooth signal detection device capable of detectingBluetooth MAC addresses of electronic devices 140 that enter theelectronic device detection zone 132. The set of images 123 may betransmitted by the camera 120 to the cloud-based computing system 116and/or the computing device 102 via the network 112. The electronicdevice identification sensor 130 may store the set of electronic deviceIDs 133 locally in a memory. The electronic device identification sensor130 may also transmit the set of electronic device IDs 133 to thecloud-based computing system 116 and/or the computing device 102 via thenetwork 112 for storage.

As noted above, the cloud-based computing system 116 may include the oneor more servers 118 that form a distributed computing architecture. Eachof the servers 118 may be any suitable computing system and may includeone or more processing devices, memory devices, data storage, and/ornetwork interface devices. The servers 118 may be in communication withone another via any suitable communication protocol. The servers 118 mayeach include at least one trusted vehicle license plate IDs database 117and at least one personal identification database 119. In someembodiments, the databases 117 and 119 may be stored on the computingdevice 102.

The trusted vehicle license plate IDs database 117 may be populated by aprocessing device adding license plate IDs of vehicles that commonlyenter the license plate detection zone 122. In some embodiments, thetrusted vehicle license plate IDs database 117 may be populated at leastin part by manual entry of license plate IDs associated with vehiclestrusted to be within the license plate detection zone 122. These licenseplate IDs may be associated with vehicles owned by neighbors in aneighborhood, or family members of the neighbors, friends of theneighbors, visitors of the neighbors, contractors hired by theneighbors, any suitable person that is trusted, etc.

The personal identification database 119 may be populated by aprocessing device adding personal identification information associatedwith electronic device IDs 133 of electronic devices carried by peoplethat commonly enter the electronic device detection zone 132 (e.g., alist of trusted electronic device IDs). In some embodiments, thepersonal identification database 119 may be populated at least in partby manual entry of personal identification information associated withelectronic device IDs 133 associated with electronic devices 140 trustedto be within the electronic device detection zone 132 (e.g., a list oftrusted electronic device IDs). These electronic device IDs 133 may beassociated with electronic devices 140 owned by neighbors in aneighborhood, or family members of the neighbors, friends of theneighbors, visitors of the neighbors, contractors hired by theneighbors, etc. Further, in some embodiments, the personalidentification database 119 may be populated by entering a list of knownsuspect individuals from the police department, people entering orexiting border checkpoints, etc.

The personal identification information for untrusted electronic deviceIDs may also be entered into the personal identification database 119.The personal identification database 119 may also be populated by aprocessing device adding personal identification information associatedwith electronic device IDs 133 of electronic devices carried by peoplethat commonly enter the facial detection zone 132 (e.g., face images oftrusted individuals). The personal identification information mayinclude names, addresses, faces, email addresses, phone numbers,electronic device identifiers associated with electronic devices ownedby the people (e.g., Bluetooth MAC addresses, WiFi MAC addresses),correlated license plate IDs with the electronic device identifiers,etc. The correlations between the license plate IDs, the electronicdevice identifiers, and/or the faces may be performed by a processingdevice using the data obtained from the cameras 120 and the electronicdevice identification sensors 130. Some of this information may beobtained from public sources, phone books, the Internet, and/orcompanies that distribute electronic devices. In some embodiments, thepersonal identification information added to the personal identificationdatabase 119 may be associated with people selected based on theirresiding in or near a certain radius of a geographic region where thezones 122 and/or 132 are set up, based on whether they are on a crimewatch list, or the like.

FIG. 2 illustrates details pertaining to various components of theillustrative system architecture 100 of FIG. 1 , according to certainembodiments of this disclosure. For example, the camera 120 includes animage capturing component 200; the electronic device identificationsensor 130 includes an electronic device ID detecting and storingcomponent 202; the server 118 includes a license plate ID detectingcomponent 204, a license plate ID comparing component 206, a suspiciousvehicle identifying component 208, and a correlating component 210. Insome embodiments, the components 204, 206, 208, and 210 may be includedin the computing device 102 executing the client application 104. Eachof the components 200, 202, 204, 206, 208, and 210 may be implemented incomputer instructions stored on one or more memory devices of theirrespective device and executed by one or more processors of theirrespective device.

With regards to the image capturing component 200, the component 200 maybe configured to capture a set of images 123 within a license platedetection zone 122. At least some of the captured images 123 mayrepresent license plates of a set of vehicles 126 appearing within thefield of view of the cameras 120. The image capturing component 200 mayconfigure one or more camera properties (e.g., zoom, focus, etc.) toobtain a clear image of the license plates. The image capturingcomponent 200 may implement various techniques to extract the licenseplate ID from the images 123, or the image capturing component 200 maytransmit the set of images 123, without analyzing the images 123, to theserver 118 via the network 112.

With regards to the electronic device ID detecting and storing component202, the component 202 may be configured to detect and store a set ofelectronic device IDs 133 of electronic devices located within one ormore electronic device detection zones 132. The electronic device IDdetecting and storing component 202 may detect a WiFi signal, cellularsignal, and/or a Bluetooth signal from the electronic device and becapable of obtaining the WiFi MAC address, cellular MAC address, and/orBluetooth MAC address of the electronic device from the signal. Theelectronic device IDs 133 may be stored locally in memory on theelectronic device identification sensor 130, and/or transmitted to theserver 118 and/or the computing device 102 via the network 112.

With regards to the license plate ID detecting component 204, thecomponent 204 may be configured to detect, using the set of images 123,a license plate ID of a vehicle 126. The license plate ID detectingcomponent 204 may perform optical character recognition (OCR), or anysuitable identifier/text extraction technique, on the set of images 123to detect the license plate IDs.

With regards to the license plate ID comparing component 206, thecomponent 206 may be configured to compare the license plate ID of thevehicle to a trusted vehicle license plate ID database 117. The licenseplate ID comparing component 206 may compare the license plate ID witheach trusted license plate ID in the trusted vehicle license plate IDdatabase 117.

With regards to the suspicious vehicle identifying component 208, thecomponent 208 may identify the vehicle 126 as a suspicious vehicle 126,the identification based at least in part on the comparison of thelicense plate ID of the vehicle 126 to the trusted vehicle license plateID database 117. If there is not a trusted license plate ID that matchesthe license plate ID of the vehicle 126, then the suspicious vehicleidentifying component 208 may identify the vehicle as a suspiciousvehicle.

With regards to the correlating component 210, the component 210 may beconfigured to correlate the license plate ID of the vehicle 126 with atleast one of the set of stored electronic device IDs 133. Correlatingthe license plate ID of the vehicle 126 with at least one of the set ofstored electronic device IDs 133 may include comparing one or more timestamps of the set of captured images 123 with one or more time stamps ofthe set of stored electronic device IDs 133. Also, correlating thelicense plate ID of the vehicle 126 with at least one of the set ofstored electronic device IDs 133 may include analyzing at least one of:(i) at least one strength of signal associated with at least one of theset of stored electronic device IDs 133, and (ii) at least one visuallyestimated distance of at least one vehicle 126 associated with at leastone of the set of stored images 123.

FIG. 3 illustrates an example method 300 for monitoring vehicle traffic,according to certain embodiments of this disclosure. The method 300 maybe performed by processing logic that may include hardware (circuitry,dedicated logic, etc.), firmware, software, or a combination of both.The method 300 and/or each of their individual functions, subroutines,or operations may be performed by one or more processors of one or moreof the devices in FIG. 1 (e.g., computing device 102, cloud-basedcomputing system 116 including servers 118, cameras 120, electronicdevice identification sensors 130) implementing the method 300. Forexample, a computing system may refer to the computing device 102 or thecloud-based computing system 116. The method 300 may be implemented ascomputer instructions that, when executed by a processing device,execute the operations. In certain implementations, the method 300 maybe performed by a single processing thread. Alternatively, the method300 may be performed by two or more processing threads, each threadimplementing one or more individual functions, routines, subroutines, oroperations of the method 300.

At block 302, a set of images 123 may be captured, using at least onecamera 120, within a license plate detection zone 122. At least some ofthe set of images 123 may represent license plates of a set of vehicles126 appearing within the camera's field of view. One or more cameraproperties (e.g., zoomed in, focused, etc.) may be configured to enablethe at least one camera 120 to obtain clear images 123 of the licenseplates.

At block 304, a set of electronic device identifiers 133 of electronicdevices 140 located within one or more electronic device detection zones132 may be detected and stored using an electronic device identificationsensor 130. In some embodiments, the electronic device identificationsensor 130 may include at least one of a WiFi signal detection device,cellular signal detection device, or a Bluetooth signal detectiondevice. In some embodiments, the set of electronic device identifiers133 may include at least one of a Bluetooth MAC address, cellular MACaddress, or a WiFi MAC address. In some embodiments, at least one of theset of stored electronic device identifiers 133 may be compared with alist of trusted device identifiers.

At block 306, a license plate ID of a vehicle 126 may be detected usingthe set of images 123. The images 123 may be filtered, rendered, and/orprocessed in any suitable manner such that the license plate IDs may beclearly detected using the set of images 123. In some embodiments,object character recognition (OCR) may be used to detect the licenseplate IDs in the set of images 123. The OCR may electronically converteach image in the set of images 123 of the license plate IDs intocomputer-encoded license plate IDs that may be stored and/or used forcomparison.

In some embodiments, a face of the individual 142 may be detected by acamera 120 in the facial detection zone 150. An image 123 may becaptured by the camera 120 and facial recognition may be performed onthe image to detect the face of the individual. The detected face and/orthe image 123 may be transmitted to the cloud-based computing system 116and/or the computing device 102.

At block 308, the license plate ID of the vehicle 126 may be compared toa database of trusted vehicle license plate IDs. In some embodiments,the database 117 of trusted vehicle license plate IDs may be populatedat least in part by adding license plate IDs of vehicles 126 thatcommonly enter the license plate detection zone 122 to the database 117of trusted vehicle license plate IDs. In some embodiments, the database117 of trusted vehicle license plate IDs may be populated at least inpart by manual entry of license plate IDs associated with vehicles 126trusted to be within the license plate detection zone 122. For example,the trusted vehicles may belong to the neighbors, family members of theneighbors, friends of the neighbors, law enforcement, and so forth.

At block 310, the vehicle may be identified as a suspicious vehicle 126.The identification may be based at least in part on the comparison ofthe license plate ID of the vehicle to the database 117 of trustedvehicle license plate IDs. For example, if the license plate ID is notmatched with a trusted license plate ID stored in the database 117 oftrusted vehicle license plate IDs, then the vehicle associated with thelicense plate ID may be identified as a suspicious vehicle 126.

At block 312, the license plate ID of the vehicle 126 may be correlatedwith at least one of the set of stored electronic device identifiers133. In some embodiments, the face of the individual 142 may also becorrelated with the license plate ID and the at least one of the set ofstored electronic device identifiers 133. In some embodiments, at leastone personal identification database 119 may be accessed. In someembodiments, correlating the license plate ID of the vehicle 126 with atleast one of the set of stored electronic device identifiers 133 mayinclude comparing one or more time stamps of the set of captured images123 with one or more time stamps of the set of stored electronic deviceidentifiers 133. In some embodiments, correlating the license plate IDof the vehicle 126 with the at least one of the set of stored electronicdevice identifiers 133 may include analyzing at least one of (i) atleast one strength of signal associated with at least one of the set ofstored electronic device identifiers 133, and (ii) at least one visuallyestimated distance of at least one vehicle associated with at least oneof the set of stored images 123.

Personal identification information of at least one suspiciousindividual may be retrieved from the at least one personalidentification database 119 by correlating information of the personalidentification database 119 with the license plate ID of the vehicle 126or at least one of the set of electronic device identifiers 133correlated with the license plate ID of the vehicle 126. The personalidentification information may also be obtained using a face detected bythe camera 120 to obtain the electronic device ID 133 and/or the licenseplate ID correlated with the face. The personal identificationinformation may include one or more of a name, a phone number, an emailaddress, a residential address, a Bluetooth MAC address, a cellular MACaddress, a WiFi MAC address, whether the suspicious individual is on acrime watch list, a criminal record of the suspicious individual, and soforth.

In some embodiments, a user interface may be displayed on one or morecomputing devices 102 of one or more neighbors when the one or morecomputing devices are executing the client application 104, and the userinterface may present a notification or alert. In some embodiments, thecomputing device 102 may present a push notification on the displayscreen and the user may provide user input (e.g., swipe the pushnotification) to expand the notification on the user interface to alarger portion of the display screen. The alert or notification mayindicate that there is a suspicious vehicle 126 identified within thezones 122 and/or 132 and may provide information pertaining to thevehicle 126 (e.g., make, model, color, license plate ID, etc.) andpersonal identification information of the suspicious individual (e.g.,name, phone number, email address, Bluetooth MAC address, cellular MACaddress, WiFi MAC address, whether the individual is on a crime watchlist, whether the individual has a criminal record, etc.).

Further, the user interface may present one or more options to performpreventative actions. The preventative actions may include contacting anelectronic device 140 of the suspicious individual using the personalidentification information. For example, a user may use a computingdevice 102 to transmit a communication (e.g., at least one text message,phone call, email, or some combination thereof) to the suspiciousindividual using the retried personal information.

In addition, the preventative actions may also include notifying lawenforcement of the suspicious vehicle and/or individual. Thispreventative action may be available if it is determined that thesuspicious individual is on a crime watch list. A suspicious vehicleprofile may be created. The suspicious vehicle profile may include thelicense plate ID of the suspicious vehicle and/or the at least onecorrelated electronic device identifiers (e.g., Bluetooth MAC address,WiFi MAC address). The user may select the notify law enforcement optionon the user interface and the computing device 102 of the user maytransmit the suspicious vehicle profile to another computing device 102of a law enforcement entity that may be logged into the clientapplication 104 using a law enforcement account.

In some embodiments, the preventative action may include activating analarm upon detection of the suspicious vehicle 126. The alarm may belocated in the neighborhood, for example, on a light pole, a tree, apole, a sign, a mailbox, a fence, or the like. The alarm may be includedin the computing device 102 of a user (e.g., a neighbor) using theclient application. The alarm may include auditory (e.g., a messageabout the suspect, a sound, etc.), visual (e.g., flash certain colors oflights), and/or haptic (e.g., vibrations) elements. In some embodiments,the severity of the alarm may change the pattern of auditory, visual,and/or haptic elements based on what kind of crimes the suspiciousindividual has committed, whether the suspicious vehicle 126 is stolen,whether the suspicious vehicle 126 matches a description of a vehicleinvolved in an Amber alert, and so forth.

FIG. 4 illustrates another example method 400 for monitoring vehicletraffic, according to certain embodiments of this disclosure. Method 400includes operations performed by one or more processing devices of oneor more devices in FIG. 1 (e.g., computing device 102, cloud-basedcomputing system 116 including servers 118, cameras 120, electronicdevice identification sensors 130) implementing the method 400. In someembodiments, one or more operations of the method 400 are implemented incomputer instructions that, when executed by a processing device,execute the operations of the steps. The method 400 may be performed inthe same or a similar manner as described above in regards to method300.

The method 400 may begin with a setup phase where various steps 402,404, 406, and/or 408 are performed to register data that may be used todetermine whether a vehicle and/or individual is suspicious. Forexample, at block 402, law evidence may be registered. The law evidencemay be obtained from a system of a law enforcement agency. For example,an application programming interface (API) of the law enforcement systemmay be exposed and API operations may be executed to obtain the lawevidence. The law evidence may indicate whether a person is on a crimewatch list 410, whether the person has a warrant, whether person has acriminal record, and/or the WiFi/Bluetooth MAC data (address)/cellulardata of electronic devices involved in incidents, as well as the ownerinformation 412 of the electronic devices. The crime watch list 410information may be used to store crime watch list 414 in a database(e.g., personal identification database 119).

At block 404, license plate registration (LPR) data may be collectedusing the one or more cameras 120 in the license plate detection zones122 as LPR raw data 416. The LPR raw data 416 may be used to obtainvehicle owner information (e.g., name, address, phone number, emailaddress) and vehicle information (e.g., license plate ID, make, model,color, year, etc.). For example, the LPR raw data 416 may include atleast the license plate ID, which may be used to search the Departmentof Motor Vehicles (DMV) to obtain the vehicle owner information and/orvehicle information. In some instances, the LPR raw data 416 may becollected from manual entry. At block 406, WiFi MAC addresses may becollected from various sources as WiFi MAC raw data 418. For example,the WiFi MAC raw data 418 may be collected from the electronic deviceidentification sensors 130 in the electronic device detection zones 132.In some instances, trusted WiFi MAC addresses may be manually obtainedfrom certain people owning electronic devices in an area covered by theelectronic device detection zones 132 and stored in a database (e.g.,personal identification database 119). In some embodiments, cellular rawdata (e.g., cellular MAC addresses) may be collected from electronicdevice identification sensors 130. At block 408, Bluetooth MAC addressesmay be collected from various sources as raw data 420. For example, theBluetooth MAC raw data 418 may be collected from the electronic deviceidentification sensors 130 in the electronic device detection zones 132.In some instances, trusted Bluetooth MAC addresses may be manuallyobtained from certain people owning electronic devices in an areacovered by the electronic device detection zones 132 and stored in adatabase (e.g., personal identification database 119). In someembodiments, the Bluetooth MAC addresses may be collected from theelectronic device identification sensors 130 at the electronic devicedetection zones 132. At block 409, face images may be collected by theone or more cameras 120 in the facial detection zones 150. Facialrecognition may be performed to detect and recognize faces in the faceimages.

At block 422, the LPR raw data 416, the WiFi MAC raw data 418, theBluetooth MAC raw data 420, the cellular raw data, and/or the face rawdata 451 may be correlated or paired to generate matched data 424. Thatis, the data from license plate ID detection, LPR systems, personalelectronic device detection, and/or facial information may be combinedto generate matched data 424 and stored in the database 117 and/or 119.In some embodiments, the license plate IDs are compared to the database119 of trusted vehicle license plate IDs to determine whether thedetected license plate ID is in the trusted vehicle license plate IDdatabase 119. If not, the vehicle 126 may be identified as a suspiciousvehicle and the license plate ID of the vehicle may be correlated withat least one of the set of stored electronic device IDs 133. This mayresult in creation of a database of detected electronic deviceidentifiers 133 correlated with license plate IDs and facial informationof individuals. Any unpaired data may be discarded after unsuccessfulpairing.

At block 426, owner data of the electronic devices and/or vehicle may beadded to the matched data 424. The owner data may include an owner ID,and/or name, address, and the like. Further, at block 428, owner's phonenumber and email may be added to the matched data. In addition,WiFi/Bluetooth MAC/cellular data and owner data 412 from the lawevidence may be included with the matched data 424 and the personalinformation of the owner to generate matched data with owner information430. Accordingly, the owner ID may be associated with combined personalinformation (e.g., name, address, phone number, email, etc.), vehicleinformation (e.g., license plate ID, make, model, color, year, vehicleowner information, etc.), and electronic device IDs 133 (e.g., WiFi MACaddress, Bluetooth MAC adder). At block 432, the matched data with ownerinformation 430 may be further processed (e.g., formatted, edited, etc.)to generate matchable data. This may conclude the setup phase.

Next, the method 400 may include a monitoring phase. During this phase,the method 400 may include monitoring steps 442, 444, and 445. At block442, WiFi MAC address monitoring may include one or more electronicdevice identification sensors 130 detecting and storing a set of WiFiMAC addresses as WiFi MAC raw data 448. In some embodiments, cellularsignal monitoring may include one or more electronic deviceidentification sensors 130 detecting and storing a set of cellular MACaddresses as cellular raw data. At block 444, Bluetooth MAC addressmonitoring may include one or more electronic device identificationsensors 130 detecting and storing a set of Bluetooth MAC addresses asBluetooth MAC raw data 450. At block 451, face monitoring may includethe one or more cameras 120 capturing face images and recognizing facesin the face images as face raw data 451. The WiFi MAC raw data 448,Bluetooth MAC raw data 450, and/or face raw data 451 may be compared tomatchable data 432 at decision block 452.

At block 452, the electronic device IDs 133 and/or faces detected by theelectronic device identification sensors 130 and/or the cameras 120 maybe compared to the matchable data 432. The matchable data 432 mayinclude personal identification information that is retrieved from atleast the personal identification database 117. That is, the detectedelectronic device IDs 133 and/or faces may be compared to the database117 and/or 119 to find any correlation of the detected electronic deviceIDs 133 and/or faces with license plate IDs.

If there is a matching electronic device ID to the detected electronicdevice ID and/or a matching face to the detected face, and there is acorrelation with a license plate ID in the database 117 and/or 119, thena suspicious vehicle 126/individual 143 may be detected. At block 456,the detected match event may be logged. At block 456, the user interfaceof the client application 104 executing on the computing device 102 maypresent an alert of the suspicious vehicle 126/individual 142. At block456, the detected notification event may be logged. At block 458, theelectronic device 140 of the suspicious individual 142 may be notifiedthat his presence is known (e.g., taunted). At block 456, the tauntingevent may be logged.

At decision block 460, the crime watch list 414 may be used to determineif the identified individual 142 is on the crime watch list 414 usingthe individual's personal information. If the individual 142 is on thewatch list 414, then at block 462, the appropriate law enforcementagency may be notified. At block 456, the law enforcement agencynotification event may be logged.

FIG. 5 illustrates example use interfaces presented on computing devicesduring monitoring vehicle traffic, according to certain embodiments ofthis disclosure. It should be noted that a user interface 500 maypresent vehicle information and electronic device information in asingle user interface. When a suspicious vehicle 126/individual 142 isdetected based on the vehicle license plate ID and/or the electronicdevice IDs 133, a notification may be presented on the user interface500 of the client application 104 executing on the computing device 102of a user (e.g., homeowner, neighbor, interested citizen). As depicted,the notification includes an alert displaying vehicle information andelectronic device information. The vehicle information includes the“Make: Jeep”, “Model: Wrangler”, “License Plate ID: ABC123”. Theelectronic device information includes “Electronic Device ID:00:11:22:33:FF:EE”, “Belongs to: John Smith”, “Phone Number:123-456-7890”. Further, the user interface 500 presents that the ownerhas a warrant out for his arrest. The notification event may be loggedin the database 117/119 or any suitable database of the system 100.

The user interface 500 includes various preventative action optionsrepresented by user interface element 502 and 504. For example, userinterface element 502 may be associated with contacting the detectedsuspicious individual 142 directly. Upon selection of the user interfaceelement 502, the user may be able to send a text message to theelectronic device 140 of the suspicious individual 142. For example, thetext message may read “Please leave the area immediately, or I willcontact law enforcement.” However, any suitable message may be sent. Themessage/taunting event may be logged in the database 117/119 or anysuitable database of the system 100.

Since the suspicious individual 142 has a warrant out for his arrestand/or is on a crime watch list, the user interface element 504 may bedisplayed that provides the option to notify law enforcement. Uponselection of the user interface element 504, a notification may betransmitted to a computing device 102 of a law enforcement agency. Thenotification may include vehicle information (e.g., “License Plate ID:ABC123”), electronic device information (e.g., “Electronic Device ID:00:11:22:33:FF:EE”), as well as location of the detection (e.g.,“Geographic Location: latitude 47.6° North and longitude 122.33° West”),and personal information (“Name: John Smith”, “Phone Number:123-456-7890”, a face of the individual 142). The law enforcement agencyevent may be logged in the database 117/119 or any suitable database ofthe system 100.

Below are example data tables that may be used to implement the systemand method for monitoring vehicle traffic disclosed herein. The datatables may include: Client and ID Tables (log ID, loginAttempts,clientUser, lawUser, billing), Data Site Info (monitoredSites,dataSites, dataGroups), Raw Collection Data (rawWiFiDataFound,rawBTDataFound, rawLPRDataFound, pairedData), Monitor Data Raw & Matched(monWiFiDataDetected, monBTDataDetected, monWiFiDataMatched,monBTDataMatched), Subject Data (subjectMatch, subjectInfo,subjectLastSeen, criminalWatchList), Notification Logs (subNotifyLog,subNotifyReplyLog, clientNotifyLog).

TABLE 1 loginID loginID username clientID idType rights email passwordlastLogin

Table 1: log ID is used for login ID/passwords, authentication andpassword resets

TABLE 2 loginAttempts loginAttempts clientID username timeStamp IPwifiRSSI wifiVendor wifiLocDet scanInt

Table 2: loginAttempts logs the number of times logins were attemptedfor both successes and failures

TABLE 3 clientUser clientUser clientID username firstName lastNamephone1 phone2 phone3 email1 email2 email3 txt1 txt2 txt3 lastUserNamedataIDs lawID monID

Table 3: clientUser includes information for each user.

TABLE 4 lawUser lawUser lawUserName lawID lawType lawPrecinct lawDeptfirstName lastName phone1 phone2 phone3 email1 email2 email3 txt1 txt2Txt3 alertType

Table 4: lawUser includes information for law enforcement personnelwanting to be notified of suspicious vehicles 126/individuals 142.

TABLE 5 billing billing clientID username package numMons optionscardType cardName cardAddr1 cardAddr2 cardCity cardState cardZIP cardNumcardExp cardID

Table 5: billing may be used for third-party billing.

TABLE 6 monitoredSites monitoredSites monID monGroupID clientID monAddr1monAddr2 monCity monState monZIP monCountry

Table 6: monitoredSites includes information for WiFi/Bluetoothmonitoring for detection, among other things.

TABLE 7 dataSites dataSites dataID dataAddr1 dataAddr2 dataCitydataState dataZIP dataCountry groupNum hwModel hwSerialNum softVersioninstallDate devLoc notes

Table 7: dataSites includes information for WiFi/Bluetooth/License PlateRegistration detection sites. These sites may supply data to databases,among other things.

TABLE 8 dataGroups dataGroups groupID groupName groupLocation groupAddr1groupAddr2 groupCity groupState groupZIP groupCountry info

Table 8: dataGroups may group data groups and monitored sites. Groupingssuch as Homeowner Associations, neighborhoods, etc.

TABLE 9 rawWiFiDataFound rawWiFiDataFound timeStamp wifiSync wifiMACwifiDevice wifiRSSI wifiVendor wifiLocDet scanInt

Table 9: rawWiFiDataFound includes raw data dump for WiFi from detectionsites used to look for matches.

TABLE 10 rawBTDataFound rawBTDataFound timeStamp btSync btMAC btNamebtRSSI btVendor btCOD btLocDet scanInt

Table 10: rawBTDataFound includes raw data dump for Bluetooth fromdetection sites used to look for matches.

TABLE 11 rawLPRDataFound rawLPRDataFound timeStamp lprPic3 lprPlatelprPic4 lprState lprPic5 lpreMake lprPic6 lprModel lprPic7 lprPlatePiclprPic8 lprPic1 lprLocDet lprPic2 scanInt

Table 11: rawLPRDataFound may include raw LPR data from detection sitesused to look for matches.

TABLE 12 pairedData pairedData pairedID btCOD timeStamp wifiLocDetlprTimeStamp btLocDet wifiTimeStamp lprLocDet btTimeStamp lprPlatePiclprPlate lprPic1 lprState lprPic2 lprMake lprPic3 lprModel lprPic4wifiMAC lprPic5 wifiDevice lprPic6 wifiVendor lprPic7 btMAC lprPic8btName subjectID btVendor

Table 12: pairedData includes matched data that may be the correlationbetween vehicle information (e.g., license plate IDs) and electronicdevice IDs 133.

TABLE 13 monWiFiDataDetected monWiFiDataDetected timestamp wifiSyncwifiMAC wifiDevice wifiRSSI wifiVendor wifiMonLoc

Table 13: monWiFiDataDetected logs of any MAC address data detectedbefore matching for WiFi.

TABLE 14 monBTDataDetected monBTDataDetected timestamp btSync btMACbtName btRSSI btVendor btCOD

Table 14: monBTDataDetected logs of any MAC address data detected beforematching for Bluetooth.

TABLE 15 monWiFiDataMatched monWiFiDataMatched pairedID timestampwifiSync wifiMAC wifiDevice wifiRSSI wifiVendor wifiMonLoc

Table 15: monWiFiDataMatched logs of any matches monitored sites find onthe database for WiFi.

TABLE 16 monBTDataMatched monBTDataMatched pairedID timestamp btSyncbtMAC btName btRSSI btVendor btCOD btMonLoc

Table 16: monBTDataMatched logs of any matches monitored sites find onthe database for Bluetooth.

TABLE 17 subjectMatch subjectMatch subjectID subjectWiFiMAC subjectBtMACtimeStamp

Table 17: subjectMatch includes a number of times subject detected inmonitored sites and data sites.

TABLE 18 subjectInfo subjectInfo subjectID subPhone1 subFirstNamesubPhone2 subLastName subPhone3 subDOB subPhone4 subAddr1 subPhone5subAddr2 subPhone6 subCity subTxt1 subState subTxt2 subZIP subTxt3

Table 18: subjectInfo includes information obtained for owner of licensevehicle.

TABLE 19 subjectLastSeen subjectLastSeen pairedID timestamp subjectIDlocID monID

Table 19: subjectLastSeen includes locations where subject was seen witha timestamp.

TABLE 20 criminalWatchList criminalWatchList subjectID crimeTypedateCommitted notifyIfDetected status

Table 20: criminalWatchList includes a criminal watch list that iscompared to subjects/individuals 142 to determine if they are a criminaland who to notify if found.

TABLE 21 subNotifyLog subNotifyLog timestamp clientID subjectIDsubPhoneTexted msgSent msgStatus

Table 21: subNotifyLog includes notifications sent to the subject todiscourage crime.

TABLE 22 subNotifyReplyLog subNotifyReplyLog timestamp clientIDsubjectID subPhoneTexted msgReceived

Table 22: subNotifyReplyLog includes any replies from the subject afternotification.

TABLE 23 clientNotifyLog clientNotifyLog timestamp clientID msgSentmsgStatus msgType numSent emailSent

Table 23: clientNotifyLog includes log of notification attempts to theclient (e.g., computing device 102 of a user).

FIG. 6 illustrates example computer system 600 which can perform any oneor more of the methods described herein, in accordance with one or moreaspects of the present disclosure. In one example, computer system 600may correspond to the computing device 102, server 118 of thecloud-based computing system 116, the cameras 120, and/or the electronicdevice identification sensors 130 of FIG. 1 . The computer system 600may be capable of executing client application 104 of FIG. 1 . Thecomputer system may be connected (e.g., networked) to other computersystems in a LAN, an intranet, an extranet, or the Internet. Thecomputer system may operate in the capacity of a server in aclient-server network environment. The computer system may be a personalcomputer (PC), a tablet computer, a wearable (e.g., wristband), aset-top box (STB), a personal Digital Assistant (PDA), a mobile phone, acamera, a video camera, an electronic device identification sensor, orany device capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that device. Further,while only a single computer system is illustrated, the term “computer”shall also be taken to include any collection of computers thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methods discussed herein.

The computer system 600 includes a processing device 602, a main memory604 (e.g., read-only memory (ROM), flash memory, dynamic random accessmemory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 606(e.g., solid state drive (SSD), flash memory, static random accessmemory (SRAM)), and a data storage device 608, which communicate witheach other via a bus 610.

Processing device 602 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 602 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 602 may also be one or more special-purpose processingdevices such as an application specific integrated circuit (ASIC), afield programmable gate array (FPGA), a digital signal processor (DSP),network processor, or the like. The processing device 602 is configuredto execute instructions for performing any of the operations and stepsdiscussed herein.

The computer system 600 may further include a network interface device612. The computer system 600 also may include a video display 614 (e.g.,a liquid crystal display (LCD) or a cathode ray tube (CRT)), one or moreinput devices 616 (e.g., a keyboard and/or a mouse), and one or morespeakers 618 (e.g., a speaker). In one illustrative example, the videodisplay 614 and the input device(s) 616 may be combined into a singlecomponent or device (e.g., an LCD touch screen).

The data storage device 616 may include a computer-readable medium 620on which the instructions 622 (e.g., implementing control system, userportal, clinical portal, and/or any functions performed by any deviceand/or component depicted in the FIGURES and described herein) embodyingany one or more of the methodologies or functions described herein isstored. The instructions 622 may also reside, completely or at leastpartially, within the main memory 604 and/or within the processingdevice 602 during execution thereof by the computer system 600. As such,the main memory 604 and the processing device 602 also constitutecomputer-readable media. The instructions 622 may further be transmittedor received over a network via the network interface device 612.

While the computer-readable storage medium 620 is shown in theillustrative examples to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

None of the description in this application should be read as implyingthat any particular element, step, or function is an essential elementthat must be included in the claim scope. The scope of patented subjectmatter is defined only by the claims. Moreover, none of the claims isintended to invoke 35 U.S.C. § 112(f) unless the exact words “means for”are followed by a participle.

What is claimed is:
 1. A system for monitoring vehicle trafficcomprising: one or more non-transitory computer-readable storage mediastoring computer-executable instructions that, when executed by one ormore processors, cause a computing system to: detect, using a pluralityof images, a license plate ID of a vehicle; compare the license plate IDof the vehicle to a database of trusted vehicle license plate IDs;identify the vehicle as a suspicious vehicle, the identification basedat least in part on the comparison of the license plate ID of thevehicle to the database of trusted vehicle license plate IDs; andcorrelate the license plate ID of the vehicle with at least one of theplurality of stored electronic device identifiers, wherein thecomputer-executable instructions further cause the computing system to:access at least one personal identification database; and retrievepersonal identification information of at least one suspiciousindividual by correlating information of the personal identificationdatabase with the license plate ID of the vehicle or at least one of aplurality of electronic device identifiers correlated with the licenseplate ID of the vehicle.
 2. The system of claim 1, wherein thecomputer-executable instructions further cause the computing system totransmit a communication to the suspicious individual using theretrieved personal information.
 3. The system of claim 1, wherein thecomputer-executable instructions further cause the computing system topopulate the database of trusted vehicle license plate IDs at least inpart by adding license plate IDs of vehicles that commonly enter thelicense plate detection zone to the database of trusted vehicle licenseplate IDs.
 4. The system of claim 1, wherein the database of trustedvehicles is populated at least in part by manual entry of license plateIDs associated with vehicles trusted to be within the license platedetection zone.
 5. The system of claim 1, wherein thecomputer-executable instructions further cause the computing system tocreate a suspicious vehicle profile, the suspicious vehicle profilecomprising the license plate ID of the vehicle and the at least onecorrelated electronic device identifiers.
 6. The system of claim 5,wherein the computer-executable instructions further cause the computingsystem to transmit the suspicious vehicle profile to a law enforcemententity.
 7. The system of claim 1, wherein the computer-executableinstructions further cause the computing system to: compare at least oneof the plurality of stored electronic device identifiers with a list oftrusted device identifiers.
 8. The system of claim 1, wherein at leastone of the plurality of stored electronic device identifiers comprises aMAC address.
 9. The system of claim 1, wherein correlating the licenseplate ID of the vehicle with at least one of the plurality of storedelectronic device identifiers comprises comparing one or more timestamps of the plurality of captured images with one or more time stampsof the plurality of stored electronic device identifiers.
 10. The systemof claim 1, wherein correlating the license plate ID of the vehicle withthe at least one of the plurality of stored electronic deviceidentifiers comprises analyzing at least one of: at least one strengthof signal associated with at least one of the plurality of storedelectronic device identifiers; and at least one visually estimateddistance of at least one vehicle associated with at least one of theplurality of stored images.
 11. The system of claim 1, furthercomprising a second camera positioned to capture a plurality of secondimages within a facial detection zone, at least some of the plurality ofsecond images representing faces of a plurality of individuals appearingwithin the second camera's field of view, wherein thecomputer-executable instructions further cause the computing system to:detect, using the plurality of second images, a face of an individual;correlate the face of the individual with the license plate ID of thevehicle and the at least one of the plurality of stored electronicdevice identifiers.
 12. A method comprising: detecting, using aplurality of images, a license plate ID of a vehicle; comparing thelicense plate ID of the vehicle to a database of trusted vehicle licenseplate IDs; identifying the vehicle as a suspicious vehicle, theidentification based at least in part on the comparison of the licenseplate ID of the vehicle to the database of trusted vehicle license plateIDs; correlating the license plate ID of the vehicle with at least oneof the plurality of stored electronic device identifiers, accessing atleast one personal identification database; and retrieving personalidentification information of at least one suspicious individual bycorrelating information of the personal identification database with thelicense plate ID of the vehicle or at least one of a plurality ofelectronic device identifiers correlated with the license plate ID ofthe vehicle.
 13. The method of claim 12, further comprising transmittinga communication to the suspicious individual using the retrievedpersonal information.
 14. The method of claim 13, wherein thetransmitted message comprises at least one text message.
 15. The methodof claim 12, further comprising populating the database of trustedvehicle license place IDs at least in part by adding license plate IDsof vehicles that commonly enter the license plate detection zone to thedatabase of trusted vehicle license plate IDs.
 16. The method of claim12, further comprising populating the database of trusted vehiclelicense plate IDs at least in part by manually entering license plateIDs associated with vehicles trusted to be within the license platedetection zone.
 17. The method of claim 12, further comprisingactivating an alarm upon detection of a suspicious vehicle.
 18. Themethod of claim 12, further comprising transmitting to a law enforcementagency one or more of the license plate ID of the vehicle or the atleast one correlated electronic device identifiers.
 19. The method ofclaim 12, wherein the plurality of images are captured by at least onecamera positioned within a license plate detection zone, at least someof the captured images representing license plates of a plurality ofvehicles appearing within the camera's field of view.
 20. The method ofclaim 12, wherein the plurality of electronic device identifiers ofelectronic devices are detected by at least one electronic deviceidentification sensor located within one or more electronic devicedetection zones.